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Background <p>Chronic Dysimmune Polyneuropathies (CDP) encompass a group of conditions characterized by autoimmune etiology targeting myelin and/or axonal components. Subgroups include Chronic Inflammatory Demyelinating Polyneuropathy (CIDP) and paraproteinemic neuropathies associated with IgM monoclonal gammopathy of undetermined significance, with anti-MAG antibodies (IgM-MGUS anti-MAG+) and without anti-MAG antibodies (IgM-MGUS anti-MAG-). Their identification is crucial for determining the most suitable treatment options, yet it poses significant challenges. In this study, an electrophysiological-based automatic classification through machine learning models is proposed.</p> Methods <p>This study included 67 patients, 29 diagnosed with CIDP, 20 with polyneuropathy associated with IgM-MGUS anti-MAG+, and 18 with CIDP-like polyneuropathy associated with IgM-MGUS anti-MAG-. Five different classification algorithms based on electrophysiological data (conduction velocity, latency, and amplitude of sensory and motor responses from different nerves) were implemented to classify three types of neuropathies and identify discriminative neurographic parameters.</p> Results <p>The best performance in stratifying the three classes was achieved by Random Forest in terms of both validation and test accuracy (86.5% and 80.6%). The predictor analysis on the best-performing model revealed the significance of F-wave latencies, distal latencies, and conduction velocities for group discrimination.</p> Conclusions <p>The study is the first to apply computational methods to identify electrophysiological parameters most frequently altered in different forms of polyneuropathy, to support clinical diagnosis and decision-making.</p>

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Electrophysiological-based automatic subgroups diagnosis of patients with chronic dysimmune polyneuropathies

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月7日

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  • Sara Ballanti,
  • Piergiuseppe Liuzzi,
  • Paolo Luca Mattiolo,
  • Maenia Scarpino,
  • Sabrina Matà,
  • Bahia Hakiki,
  • Francesca Cecchi,
  • Calogero Maria Oddo,
  • Andrea Mannini,
  • Antonello Grippo

摘要

Background

Chronic Dysimmune Polyneuropathies (CDP) encompass a group of conditions characterized by autoimmune etiology targeting myelin and/or axonal components. Subgroups include Chronic Inflammatory Demyelinating Polyneuropathy (CIDP) and paraproteinemic neuropathies associated with IgM monoclonal gammopathy of undetermined significance, with anti-MAG antibodies (IgM-MGUS anti-MAG+) and without anti-MAG antibodies (IgM-MGUS anti-MAG-). Their identification is crucial for determining the most suitable treatment options, yet it poses significant challenges. In this study, an electrophysiological-based automatic classification through machine learning models is proposed.

Methods

This study included 67 patients, 29 diagnosed with CIDP, 20 with polyneuropathy associated with IgM-MGUS anti-MAG+, and 18 with CIDP-like polyneuropathy associated with IgM-MGUS anti-MAG-. Five different classification algorithms based on electrophysiological data (conduction velocity, latency, and amplitude of sensory and motor responses from different nerves) were implemented to classify three types of neuropathies and identify discriminative neurographic parameters.

Results

The best performance in stratifying the three classes was achieved by Random Forest in terms of both validation and test accuracy (86.5% and 80.6%). The predictor analysis on the best-performing model revealed the significance of F-wave latencies, distal latencies, and conduction velocities for group discrimination.

Conclusions

The study is the first to apply computational methods to identify electrophysiological parameters most frequently altered in different forms of polyneuropathy, to support clinical diagnosis and decision-making.